Spam Classifier Web Application

Overview

This project is a web application built with Flask, incorporating a machine learning model for spam classification. Users can input text, and the application predicts whether the text is spam or not. The application also logs predictions to a MySQL database and provides a user interface for viewing prediction logs.

Getting Started

Local Development

1. Clone the repository:

   git clone https://github.com/yourusername/spam-classifier.git
   cd spam-classifier

2. Install dependencies:

   pip install -r requirements.txt

3. Run the Flask application locally:

   python app.py

   The application will be accessible at http://localhost:5000.

Docker Deployment

1. Build the Docker image:

   docker build -t spam-app:v1 .

2. Run the Docker container:

   docker run -p 5000:5000 spam-app:v1

   The application will be accessible at http://localhost:5000.

Kubernetes Deployment

1. Apply the Kubernetes deployment and service YAML files:

   kubectl create --filename deployment.yaml
   kubectl create --filename service.yaml

2. Access the application:

   Get the external IP address:

   kubectl get services

   Access the application using the external IP address.

Project Structure

• app.py: Flask application for spam classification.
• index.html: HTML template for the main page.
• db_logs.html: HTML template for viewing prediction logs.
• requirements.txt: List of Python dependencies.
• Dockerfile: Docker configuration for building the application image.
• deployment.yaml: Kubernetes deployment configuration.
• service.yaml: Kubernetes service configuration.

Configuration

• app.config['SQLALCHEMY_DATABASE_URI']: Database URI for SQLAlchemy. Modify this for your database configuration.

Dependencies

• Flask==3.0.0
• scikit-learn==1.3.2
• pandas==2.0.3
• torch==1.10.0+cpu
• Werkzeug==3.0.1
• urllib3==2.0.7
• Flask-SQLAlchemy==3.1.1
• PyMySQL==1.0.3
• SQLAlchemy==2.0.23
• mysql-connector-python==8.2.0